exec_comp_for_test.py¶

class
openmdao.test_suite.components.exec_comp_for_test.
ExecComp4Test
(exprs, nl_delay=0.01, lin_delay=0.01, req_procs=(1, 1), fail_rank=1, fails=(), fail_hard=False, **kwargs)[source]¶ Bases:
openmdao.components.exec_comp.ExecComp
A version of ExecComp for benchmarking and testing.
Parameters:  exprs : str or list of str
The expressions that determine the inputs and outputs of this component.
 nl_delay : float(0.01)
The sleep time in seconds that will occur when solve_nonlinear is called.
 lin_delay : float(0.01)
The sleep time in seconds that will occur when apply_linear is called.
 rec_procs : tuple of the form (minprocs, maxprocs)
Minimum and maximun MPI processes usable by this component.
 fail_rank : int or collection of int (0)
Rank (if running under MPI) or worker number (if running under multiprocessing) where failures will be initiated.
 fails : list or tuple of int
If the current self.num_nl_solves matches any of these, then this component will raise an exception.
 fail_hard : bool(False)
If True and fails is not empty, this component will raise a RuntimeError when a failure is induced. Otherwise, an AnalysisError will be raised.

__init__
(exprs, nl_delay=0.01, lin_delay=0.01, req_procs=(1, 1), fail_rank=1, fails=(), fail_hard=False, **kwargs)[source]¶ Create a <Component> using only an expression string.
Given a list of assignment statements, this component creates input and output variables at construction time. All variables appearing on the lefthand side of an assignment are outputs, and the rest are inputs. Each variable is assumed to be of type float unless the initial value for that variable is supplied in **kwargs. Derivatives are calculated using complex step.
The following functions are available for use in expressions:
Function Description abs(x) Absolute value of x acos(x) Inverse cosine of x acosh(x) Inverse hyperbolic cosine of x arange(start, stop, step) Array creation arccos(x) Inverse cosine of x arccosh(x) Inverse hyperbolic cosine of x arcsin(x) Inverse sine of x arcsinh(x) Inverse hyperbolic sine of x arctan(x) Inverse tangent of x asin(x) Inverse sine of x asinh(x) Inverse hyperbolic sine of x atan(x) Inverse tangent of x cos(x) Cosine of x cosh(x) Hyperbolic cosine of x dot(x, y) Dotproduct of x and y e Euler’s number erf(x) Error function erfc(x) Complementary error function exp(x) Exponential function expm1(x) exp(x)  1 factorial(x) Factorial of all numbers in x fmax(x, y) Elementwise maximum of x and y fmin(x, y) Elementwise minimum of x and y inner(x, y) Inner product of arrays x and y isinf(x) Elementwise detection of np.inf isnan(x) Elementwise detection of np.nan kron(x, y) Kronecker product of arrays x and y linspace(x, y, N) Numpy linear spaced array creation log(x) Natural logarithm of x log10(x) Base10 logarithm of x log1p(x) log(1+x) matmul(x, y) Matrix multiplication of x and y maximum(x, y) Elementwise maximum of x and y minimum(x, y) Elementwise minimum of x and y ones(N) Create an array of ones outer(x, y) Outer product of x and y pi Pi power(x, y) Elementwise x**y prod(x) The product of all elements in x sin(x) Sine of x sinh(x) Hyperbolic sine of x sum(x) The sum of all elements in x tan(x) Tangent of x tanh(x) Hyperbolic tangent of x tensordot(x, y) Tensor dot product of x and y zeros(N) Create an array of zeros Parameters:  exprs : str, tuple of str or list of str
An assignment statement or iter of them. These express how the outputs are calculated based on the inputs. In addition to standard Python operators, a subset of numpy and scipy functions is supported.
 vectorize : bool
If True, treat all array/array partials as diagonal if both arrays have size > 1. All arrays with size > 1 must have the same flattened size or an exception will be raised.
 **kwargs : dict of named args
Initial values of variables can be set by setting a named arg with the var name. If the value is a dict it is assumed to contain metadata. To set the initial value in addition to other metadata, assign the initial value to the ‘value’ entry of the dict.
Notes
If a variable has an initial value that is anything other than 1.0, either because it has a different type than float or just because its initial value is != 1.0, you must use a keyword arg to set the initial value. For example, let’s say we have an ExecComp that takes an array ‘x’ as input and outputs a float variable ‘y’ which is the sum of the entries in ‘x’.
import numpy from openmdao.api import ExecComp excomp = ExecComp('y=sum(x)', x=numpy.ones(10,dtype=float))
In this example, ‘y’ would be assumed to be the default type of float and would be given the default initial value of 1.0, while ‘x’ would be initialized with a size 10 float array of ones.
If you want to assign certain metadata for ‘x’ in addition to its initial value, you can do it as follows:
excomp = ExecComp('y=sum(x)', x={'value': numpy.ones(10,dtype=float), 'units': 'ft'})

add_constraint
(name, lower=None, upper=None, equals=None, ref=None, ref0=None, adder=None, scaler=None, indices=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a constraint variable to this system.
Parameters:  name : string
Name of the response variable in the system.
 lower : float or ndarray, optional
Lower boundary for the variable
 upper : float or ndarray, optional
Upper boundary for the variable
 equals : float or ndarray, optional
Equality constraint value for the variable
 ref : float or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0 : float or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 indices : sequence of int, optional
If variable is an array, these indicate which entries are of interest for this particular response. These may be positive or negative integers.
 linear : bool
Set to True if constraint is linear. Default is False.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 cache_linear_solution : bool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
Notes
The response can be scaled using ref and ref0. The argument
ref0
represents the physical value when the scaled value is 0. The argumentref
represents the physical value when the scaled value is 1.

add_design_var
(name, lower=None, upper=None, ref=None, ref0=None, indices=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a design variable to this system.
Parameters:  name : string
Name of the design variable in the system.
 lower : float or ndarray, optional
Lower boundary for the param
 upper : upper or ndarray, optional
Upper boundary for the param
 ref : float or ndarray, optional
Value of design var that scales to 1.0 in the driver.
 ref0 : float or ndarray, optional
Value of design var that scales to 0.0 in the driver.
 indices : iter of int, optional
If a param is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 cache_linear_solution : bool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
Notes
The response can be scaled using ref and ref0. The argument
ref0
represents the physical value when the scaled value is 0. The argumentref
represents the physical value when the scaled value is 1.

add_discrete_input
(name, val, desc='')¶ Add a discrete input variable to the component.
Parameters:  name : str
name of the variable in this component’s namespace.
 val : a picklable object
The initial value of the variable being added.
 desc : str
description of the variable
Returns:  dict
metadata for added variable

add_discrete_output
(name, val, desc='')¶ Add an output variable to the component.
Parameters:  name : str
name of the variable in this component’s namespace.
 val : a picklable object
The initial value of the variable being added.
 desc : str
description of the variable.
Returns:  dict
metadata for added variable

add_input
(name, val=1.0, shape=None, src_indices=None, flat_src_indices=None, units=None, desc='')¶ Add an input variable to the component.
Parameters:  name : str
name of the variable in this component’s namespace.
 val : float or list or tuple or ndarray or Iterable
The initial value of the variable being added in userdefined units. Default is 1.0.
 shape : int or tuple or list or None
Shape of this variable, only required if src_indices not provided and val is not an array. Default is None.
 src_indices : int or list of ints or tuple of ints or int ndarray or Iterable or None
The global indices of the source variable to transfer data from. A value of None implies this input depends on all entries of source. Default is None. The shapes of the target and src_indices must match, and form of the entries within is determined by the value of ‘flat_src_indices’.
 flat_src_indices : bool
If True, each entry of src_indices is assumed to be an index into the flattened source. Otherwise each entry must be a tuple or list of size equal to the number of dimensions of the source.
 units : str or None
Units in which this input variable will be provided to the component during execution. Default is None, which means it is unitless.
 desc : str
description of the variable
Returns:  dict
metadata for added variable

add_objective
(name, ref=None, ref0=None, index=None, adder=None, scaler=None, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a response variable to this system.
Parameters:  name : string
Name of the response variable in the system.
 ref : float or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0 : float or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 index : int, optional
If variable is an array, this indicates which entry is of interest for this particular response. This may be a positive or negative integer.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 cache_linear_solution : bool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
Notes
The objective can be scaled using scaler and adder, where
\[x_{scaled} = scaler(x + adder)\]or through the use of ref/ref0, which map to scaler and adder through the equations:
\[ \begin{align}\begin{aligned}0 = scaler(ref_0 + adder)\\1 = scaler(ref + adder)\end{aligned}\end{align} \]which results in:
\[ \begin{align}\begin{aligned}adder = ref_0\\scaler = \frac{1}{ref + adder}\end{aligned}\end{align} \]

add_output
(name, val=1.0, shape=None, units=None, res_units=None, desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=None)¶ Add an output variable to the component.
For ExplicitComponent, res_ref defaults to the value in res unless otherwise specified.
Parameters:  name : str
name of the variable in this component’s namespace.
 val : float or list or tuple or ndarray
The initial value of the variable being added in userdefined units. Default is 1.0.
 shape : int or tuple or list or None
Shape of this variable, only required if val is not an array. Default is None.
 units : str or None
Units in which the output variables will be provided to the component during execution. Default is None, which means it has no units.
 res_units : str or None
Units in which the residuals of this output will be given to the user when requested. Default is None, which means it has no units.
 desc : str
description of the variable.
 lower : float or list or tuple or ndarray or None
lower bound(s) in userdefined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.
 upper : float or list or tuple or ndarray or None
upper bound(s) in userdefined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.
 ref : float
Scaling parameter. The value in the userdefined units of this output variable when the scaled value is 1. Default is 1.
 ref0 : float
Scaling parameter. The value in the userdefined units of this output variable when the scaled value is 0. Default is 0.
 res_ref : float
Scaling parameter. The value in the userdefined res_units of this output’s residual when the scaled value is 1. Default is None, which means residual scaling matches output scaling.
Returns:  dict
metadata for added variable

add_recorder
(recorder, recurse=False)¶ Add a recorder to the driver.
Parameters:  recorder : <CaseRecorder>
A recorder instance.
 recurse : boolean
Flag indicating if the recorder should be added to all the subsystems.

add_response
(name, type_, lower=None, upper=None, equals=None, ref=None, ref0=None, indices=None, index=None, adder=None, scaler=None, linear=False, parallel_deriv_color=None, vectorize_derivs=False, cache_linear_solution=False)¶ Add a response variable to this system.
The response can be scaled using ref and ref0. The argument
ref0
represents the physical value when the scaled value is 0. The argumentref
represents the physical value when the scaled value is 1.Parameters:  name : string
Name of the response variable in the system.
 type_ : string
The type of response. Supported values are ‘con’ and ‘obj’
 lower : float or ndarray, optional
Lower boundary for the variable
 upper : upper or ndarray, optional
Upper boundary for the variable
 equals : equals or ndarray, optional
Equality constraint value for the variable
 ref : float or ndarray, optional
Value of response variable that scales to 1.0 in the driver.
 ref0 : upper or ndarray, optional
Value of response variable that scales to 0.0 in the driver.
 indices : sequence of int, optional
If variable is an array, these indicate which entries are of interest for this particular response.
 index : int, optional
If variable is an array, this indicates which entry is of interest for this particular response.
 adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder is first in precedence.
 scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler is second in precedence.
 linear : bool
Set to True if constraint is linear. Default is False.
 parallel_deriv_color : string
If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
 vectorize_derivs : bool
If True, vectorize derivative calculations.
 cache_linear_solution : bool
If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.

check_config
(logger)¶ Perform optional error checks.
Parameters:  logger : object
The object that manages logging output.

cleanup
()¶ Clean up resources prior to exit.

compute
(inputs, outputs)[source]¶ Execute this component’s assignment statements.
Parameters:  inputs : Vector
Vector containing inputs.
 outputs : Vector
Vector containing outputs.

compute_jacvec_product
(inputs, d_inputs, d_outputs, mode)¶ Compute jacvector product. The model is assumed to be in an unscaled state.
 If mode is:
‘fwd’: d_inputs > d_outputs
‘rev’: d_outputs > d_inputs
Parameters:  inputs : Vector
unscaled, dimensional input variables read via inputs[key]
 d_inputs : Vector
see inputs; product must be computed only if var_name in d_inputs
 d_outputs : Vector
see outputs; product must be computed only if var_name in d_outputs
 mode : str
either ‘fwd’ or ‘rev’

compute_partials
(inputs, partials)[source]¶ Use complex step method to update the given Jacobian.
Parameters:  inputs : Vector
Vector containing parameters. (p)
 partials : Jacobian
Contains subjacobians.

declare_partials
(of, wrt, dependent=True, rows=None, cols=None, val=None, method='exact', step=None, form=None, step_calc=None)¶ Declare information about this component’s subjacobians.
Parameters:  of : str or list of str
The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern.
 wrt : str or list of str
The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.
 dependent : bool(True)
If False, specifies no dependence between the output(s) and the input(s). This is only necessary in the case of a sparse global jacobian, because if ‘dependent=False’ is not specified and declare_partials is not called for a given pair, then a dense matrix of zeros will be allocated in the sparse global jacobian for that pair. In the case of a dense global jacobian it doesn’t matter because the space for a dense subjac will always be allocated for every pair.
 rows : ndarray of int or None
Row indices for each nonzero entry. For sparse subjacobians only.
 cols : ndarray of int or None
Column indices for each nonzero entry. For sparse subjacobians only.
 val : float or ndarray of float or scipy.sparse
Value of subjacobian. If rows and cols are not None, this will contain the values found at each (row, col) location in the subjac.
 method : str
The type of approximation that should be used. Valid options include: ‘fd’: Finite Difference, ‘cs’: Complex Step, ‘exact’: use the component defined analytic derivatives. Default is ‘exact’.
 step : float
Step size for approximation. Defaults to None, in which case the approximation method provides its default value.
 form : string
Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Defaults to None, in which case the approximation method provides its default value.
 step_calc : string
Step type for finite difference, can be ‘abs’ for absolute’, or ‘rel’ for relative. Defaults to None, in which case the approximation method provides its default value.

distributed
¶ Provide ‘distributed’ property for backwards compatibility.
Returns:  bool
reference to the ‘distributed’ option.

get_constraints
(recurse=True)¶ Get the Constraint settings from this system.
Retrieve the constraint settings for the current system as a dict, keyed by variable name.
Parameters:  recurse : bool, optional
If True, recurse through the subsystems and return the path of all constraints relative to the this system.
Returns:  dict
The constraints defined in the current system.

get_design_vars
(recurse=True, get_sizes=True)¶ Get the DesignVariable settings from this system.
Retrieve all design variable settings from the system and, if recurse is True, all of its subsystems.
Parameters:  recurse : bool
If True, recurse through the subsystems and return the path of all design vars relative to the this system.
 get_sizes : bool, optional
If True, compute the size of each response.
Returns:  dict
The design variables defined in the current system and, if recurse=True, its subsystems.

get_linear_vectors
(vec_name='linear')¶ Return the linear inputs, outputs, and residuals vectors.
Parameters:  vec_name : str
Name of the linear righthandside vector. The default is ‘linear’.
Returns:  (inputs, outputs, residuals) : tuple of <Vector> instances
Yields the inputs, outputs, and residuals linear vectors for vec_name.

get_nonlinear_vectors
()¶ Return the inputs, outputs, and residuals vectors.
Returns:  (inputs, outputs, residuals) : tuple of <Vector> instances
Yields the inputs, outputs, and residuals nonlinear vectors.

get_objectives
(recurse=True)¶ Get the Objective settings from this system.
Retrieve all objectives settings from the system as a dict, keyed by variable name.
Parameters:  recurse : bool, optional
If True, recurse through the subsystems and return the path of all objective relative to the this system.
Returns:  dict
The objectives defined in the current system.

get_responses
(recurse=True, get_sizes=True)¶ Get the response variable settings from this system.
Retrieve all response variable settings from the system as a dict, keyed by variable name.
Parameters:  recurse : bool, optional
If True, recurse through the subsystems and return the path of all responses relative to the this system.
 get_sizes : bool, optional
If True, compute the size of each response.
Returns:  dict
The responses defined in the current system and, if recurse=True, its subsystems.

initialize
()¶ Perform any onetime initialization run at instantiation.

is_active
()¶ Determine if the system is active on this rank.
Returns:  bool
If running under MPI, returns True if this System has a valid communicator. Always returns True if not running under MPI.

linear_solver
¶ Get the linear solver for this system.

list_inputs
(values=True, units=False, hierarchical=True, print_arrays=False, out_stream=<object object>)¶ Return and optionally log a list of input names and other optional information.
If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.
Parameters:  values : bool, optional
When True, display/return input values. Default is True.
 units : bool, optional
When True, display/return units. Default is False.
 hierarchical : bool, optional
When True, human readable output shows variables in hierarchical format.
 print_arrays : bool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
 out_stream : filelike object
Where to send human readable output. Default is sys.stdout. Set to None to suppress.
Returns:  list
list of input names and other optional information about those inputs

list_outputs
(explicit=True, implicit=True, values=True, prom_name=False, residuals=False, residuals_tol=None, units=False, shape=False, bounds=False, scaling=False, hierarchical=True, print_arrays=False, out_stream=<object object>)¶ Return and optionally log a list of output names and other optional information.
If the model is parallel, only the local variables are returned to the process. Also optionally logs the information to a user defined output stream. If the model is parallel, the rank 0 process logs information about all variables across all processes.
Parameters:  explicit : bool, optional
include outputs from explicit components. Default is True.
 implicit : bool, optional
include outputs from implicit components. Default is True.
 values : bool, optional
When True, display/return output values. Default is True.
 prom_name : bool, optional
When True, display/return the promoted name of the variable. Default is False.
 residuals : bool, optional
When True, display/return residual values. Default is False.
 residuals_tol : float, optional
If set, limits the output of list_outputs to only variables where the norm of the resids array is greater than the given ‘residuals_tol’. Default is None.
 units : bool, optional
When True, display/return units. Default is False.
 shape : bool, optional
When True, display/return the shape of the value. Default is False.
 bounds : bool, optional
When True, display/return bounds (lower and upper). Default is False.
 scaling : bool, optional
When True, display/return scaling (ref, ref0, and res_ref). Default is False.
 hierarchical : bool, optional
When True, human readable output shows variables in hierarchical format.
 print_arrays : bool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
 out_stream : filelike
Where to send human readable output. Default is sys.stdout. Set to None to suppress.
Returns:  list
list of output names and other optional information about those outputs

ln_solver
¶ Get the linear solver for this system.

metadata
¶ Get the options for this System.

nl_solver
¶ Get the nonlinear solver for this system.

nonlinear_solver
¶ Get the nonlinear solver for this system.

reconfigure
()¶ Perform reconfiguration.
Returns:  bool
If True, reconfiguration is to be performed.

record_iteration
()¶ Record an iteration of the current System.

resetup
(setup_mode='full')¶ Public wrapper for _setup that reconfigures after an initial setup has been performed.
Parameters:  setup_mode : str
Must be one of ‘full’, ‘reconf’, or ‘update’.

run_apply_linear
(vec_names, mode, scope_out=None, scope_in=None)¶ Compute jacvec product.
This calls _apply_linear, but with the model assumed to be in an unscaled state.
Parameters:  vec_names : [str, …]
list of names of the righthandside vectors.
 mode : str
‘fwd’ or ‘rev’.
 scope_out : set or None
Set of absolute output names in the scope of this matvec product. If None, all are in the scope.
 scope_in : set or None
Set of absolute input names in the scope of this matvec product. If None, all are in the scope.

run_apply_nonlinear
()¶ Compute residuals.
This calls _apply_nonlinear, but with the model assumed to be in an unscaled state.

run_linearize
(sub_do_ln=True)¶ Compute jacobian / factorization.
This calls _linearize, but with the model assumed to be in an unscaled state.
Parameters:  sub_do_ln : boolean
Flag indicating if the children should call linearize on their linear solvers.

run_solve_linear
(vec_names, mode)¶ Apply inverse jac product.
This calls _solve_linear, but with the model assumed to be in an unscaled state.
Parameters:  vec_names : [str, …]
list of names of the righthandside vectors.
 mode : str
‘fwd’ or ‘rev’.
Returns:  boolean
Failure flag; True if failed to converge, False is successful.
 float
relative error.
 float
absolute error.

run_solve_nonlinear
()¶ Compute outputs.
This calls _solve_nonlinear, but with the model assumed to be in an unscaled state.
Returns:  boolean
Failure flag; True if failed to converge, False is successful.
 float
relative error.
 float
absolute error.

set_check_partial_options
(wrt, method='fd', form=None, step=None, step_calc=None)¶ Set options that will be used for checking partial derivatives.
Parameters:  wrt : str or list of str
The name or names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.
 method : str
Method for check: “fd” for finite difference, “cs” for complex step.
 form : str
Finite difference form for check, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.
 step : float
Step size for finite difference check. Leave undeclared to keep unchanged from previous or default value.
 step_calc : str
Type of step calculation for check, can be “abs” for absolute (default) or “rel” for relative. Leave undeclared to keep unchanged from previous or default value.

set_initial_values
()¶ Set all input and output variables to their declared initial values.

setup
()¶ Set up variable name and metadata lists.

system_iter
(include_self=False, recurse=True, typ=None)¶ Yield a generator of local subsystems of this system.
Parameters:  include_self : bool
If True, include this system in the iteration.
 recurse : bool
If True, iterate over the whole tree under this system.
 typ : type
If not None, only yield Systems that match that are instances of the given type.